What is data modeling?

In today’s data-driven world, data modeling is crucial for organisations that want to make informed business decisions and gain a competitive edge in their sectors and industries. Whether they’re managing large-scale databases or seeking valuable insights from within their customer and client data, understanding the principles and techniques of data modeling is essential for success. 

At its core, data modeling is the process of creating a visual representation of data, including data structures and relationships. It involves transforming real-world data entities – and their interactions – into a structured format, one that can be easily understood and managed by people and within information systems.

Why is data modeling important?

Data modeling plays a pivotal role in managing and utilising data effectively. It enables and encourages business to:

• Unlock the full potential of their data by uncovering new insights.
• Drive business intelligence initiatives using robust data elements.
• Ensure data can be understood at all levels within an organisation.
• Improve data governance and modeling methodology.

With the continuous growth of data science, especially in areas such as artificial intelligence and machine learning, data modeling will continue to evolve in order to meet the needs of the modern digital landscape.

Types of data models

To understand data models, it helps to first understand the three categories that data models fall into. These categories are:

1. Conceptual. The conceptual data model provides a high-level view of an entire database system, capturing business rules and requirements and defining its entities, relationships, and attributes – without going into technical details.
2. Logical. The logical data model translates the conceptual model into a more detailed representation. It identifies entities, attributes, and relationships, but also enables a deeper understanding of the data structure. It does this while remaining independent of any specific database management system (DBMS).
3. Physical. The physical data model represents the implementation of the logical model within a particular DBMS. It defines the database structure, including tables, columns, indexes, and constraints, catering to the specific requirements of the chosen DBMS.

Examples of data models

Entity-Relationship (ER) model

An ER diagram is a widely used data modeling technique that visually represents the entities, attributes, and relationships within a system. For example, an ER diagram could include entities like “Customer,” “Order,” and “Product,” along with their respective attributes and relationships.

Relational data model

A relational data model is a common approach used to organise different kinds of data into tables with rows and columns. Each table represents an entity, and relationships are established through primary keys and foreign keys. For example, tables could be created for customers, orders, and products, along with their associated attributes.

Hierarchical data model

A hierarchical data model typically represents data sets in a tree, highlighting what’s known as one-to-many relationships and dependencies between data points through parent-child relationship diagrams.

Object-oriented data model

An object-oriented data model groups data into classes, with each class having associated features. 

Dimensional data model

Dimensional modeling uses a snowflake or star schema to make it easy for data and business analysts to organise and analyse data in data warehouses.

Understanding different types of data

Data comes in various forms, each requiring specific considerations during the modeling process.

Structured data

Structured data refers to organised and well-defined data with a fixed schema. It can be easily stored and managed within traditional relational databases, allowing for efficient querying and analysis.

Unstructured data

Unstructured data, which can include things like text documents, images, and videos, typically lacks a predefined structure, and requires different data storage and processing approaches. 

Techniques like NoSQL databases and big data frameworks are commonly used to handle unstructured data.

Semi-structured data

Semi-structured data exhibits some structure but doesn’t conform strictly to a predefined schema. Examples include XML and JSON documents. Proper modeling and design can enable efficient storage and retrieval of semi-structured data.

Tools for data modeling and design

Various data modeling tools are available to aid in the data modeling and design process. These tools provide features like visual representation, diagramming, and collaboration, making it easier to create, manage, and communicate complex data models.

Popular tools include:

• SQL-based modeling tools.
• UML-based tools.
• Specialised data modeling software.

The data modeling process

To develop an effective data model, it’s essential to follow a systematic approach. This approach should include a number of key steps:

Capture and understand business needs

Collaborate with business stakeholders, analysts, and data architects to identify the business requirements and define the scope of the data model.

Gather data requirements

Document the data requirements by analysing business processes, use cases, and existing data sources.

Conceptualise the model

Create a conceptual data model that represents the entities, relationships, and data attributes at a high level.

Refine the logical model

Translate the conceptual model into a logical data model, adding more detail and specificity while remaining database-agnostic.

Implement the physical model

Transform the logical model into a physical data model, considering performance optimisation, database model, and data integrity.

Validate and iterate

Validate the data model with business stakeholders and iterate as necessary to ensure alignment with business needs.

The difference between data modeling and database design

Data modeling and database design are linked, but separate.

While data modeling focuses on creating a conceptual and logical representation of data, database design is concerned with the actual implementation of the data model within a specific DBMS. Data design focuses on creating an efficient and scalable database structure based on the conceptual and logical models derived from data modeling. Data modeling, meanwhile, provides the foundation for effective database design, ensuring a well-structured and scalable database system.

Unlock the full potential of data

Learn to lead with data by studying the 100% online MSc Management with Data Analytics at Keele University. This flexible, part-time programme has been designed for leaders and aspiring leaders who are aiming to progress into more senior roles, and who want to develop a firm understanding of the strategic and operational challenges in running an organisation, particularly through the lens of harnessing data for success.

One of the key modules on this programme examines visualisation for data analytics, providing you with a comprehensive understanding of the use of data analytics within areas such as health, security, science and business. The module equips you with a variety of data science visualisation techniques to enable you to make sense of the emergence and growth of big data. 

Another key module explores data analytics and databases, covering a variety of the tools and statistical techniques needed to make sense of the exponential growth of big data. You will develop knowledge of advanced analytics and statistical modeling techniques, and evaluate their applicability to different types of problems.

Other areas of study include:

• Operations and supply chain management.
• Financial statement analysis.
• Strategic marketing.
• Managing people and organisations.
• Systems design and programming.